The methods we compare against are NPBG [1]}, NRW [2]}, Pulsar [3]}, and Synsin [4]}.
The latter two are general differentiable rendering front-ends that have been adapted by us to the novel view synthesis problem.
We use them to render the point-geometry into a neural image that is then passed through a deep neural network for the final output.
They can be seen similar to our approach and NPBG with the difference of using sphere and splat-based rendering instead of a multi-layer one-pixel point rendering.
Pulsar and Synsin only support pinhole cameras.
Therefore we train them on the undistorted images and camera parameters provided by COLMAP.
During evaluation the synthesized undistorted images are distorted again and compared to the ground truth.
NPBG, NRW, and our approach are trained on the original distorted images.
These methods use one-pixel point rendering, which natively supports all camera models.
